Assessment-Based Optimization of Distillation Parameters

Abstract

The range of high-quality alcoholic beverages could be expanded by unconventional raw materials, e.g., bakery waste. Any new technology requires optimization of operating parameters at each production stage. The sensory properties of an alcoholic drink depend on the distillation mode. However, food science knows no objective methods for optimizing distillation parameters based on the biochemical composition of the raw material. The research objective was to develop a new methodology for optimizing the distillation procedure for alcoholic drinks based on unconventional raw materials. The research featured distillates obtained from industrial samples of bakery waste. The variable factors included the distillation rate, which ranged from 5 to 17 cm3/min, and the wort acidification degree, which was pH 6.0–2.0. The composition and mass concentration of the main volatile components were determined by gas chromatography using a Thermo Trace GC Ultra device (Thermo, USA) with a flame ionization detector. The sensory evaluation was performed by a panel of qualified experts. The single-factor experiment showed that the distillation rate and the wort acidification degree affected the concentration of each volatile component in the distillate. Using the method of pairwise correlation coefficients, the authors identified the most significant parameters: mass concentration of 1-propanol, phenylethyl alcohol, ethyl lactate, total enanthic esters, total enanthic esters vs. total esters, concentration of ethyl lactate vs. total enanthic esters, isobutanol concentration vs.1-propanol concentration. The linear pair correlation coefficients were calculated for these selected indicators, and the effect of each parameter on the sensory profile was represented as a regression model. The optimal operating parameters were determined by extremization of a two-variable function: pH 4.4 ± 0.2, speed 9.5 ± 1.0 cm3/min. The new methodology provided for the following sequence of operations: determining the significance of the variable factor; selecting the evaluation parameters based on a single-factor experiment; determining the interaction; developing a regression model. This method can be used to calculate the optimal technological distillation parameters for other raw materials.